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首页> 外文会议>ICCEE 2010;International conference on computer and electrical engineering >Eigenspace-based speaker adaptation methods in Persian speech recognition systems
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Eigenspace-based speaker adaptation methods in Persian speech recognition systems

机译：波斯语语音识别系统中基于特征空间的说话人自适应方法

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Among speaker adaptation algorithms, eigenvoice (EV) and eigenspace-based MLLR (EMLLR) adaptation approaches have been proposed for rapid adaptation with very limited adaptation data. In these methods, a speaker adapted model is constrained to be a weighted combination of some orthogonal basis vectors. In this manner, both the number of parameters to be estimated from the adaptation data, and the required adaption data dramatically decrease. Although these two algorithms have an acceptable performance for adaptation data in the range of 5 to 10 seconds of speech wave, availability of a large amount of adaption data does not necessarily lead to more efficient models. Experimental results of applying EV and EMLLR adaptation algorithms on FARSDAT database discussed in the paper show that by a limited supervised adaptation data (5-10 seconds), these methods lead to respectively 5.9% and 5.3% improvement in phoneme recognition rate. Furthermore, they yield about 4% improvement in unsupervised adaptation, where the common speaker adaptation methods such as MLLR, cannot work efficiently through a limited supervised or unsupervised adaptation data. In addition, in this paper, the development of EV performance in a large amount of adaptation data is achieved by segmenting the eigenspace based on model characteristics.
机译：在说话人自适应算法中，已经提出了本征语音（EV）和基于本征空间的MLLR（EMLLR）自适应方法，用于使用非常有限的自适应数据进行快速自适应。在这些方法中，说话者适应模型被约束为一些正交基矢量的加权组合。以这种方式，将从适配数据估计的参数的数量和所需的适配数据都大大减少。尽管这两种算法对于5到10秒的语音波范围内的自适应数据都具有可接受的性能，但是大量自适应数据的可用性并不一定会导致更有效的模型。本文讨论的将EV和EMLLR自适应算法应用于FARSDAT数据库的实验结果表明，通过有限的监督自适应数据（5-10秒），这些方法分别使音素识别率提高了5.9％和5.3％。此外，它们可在无监督适应中提高约4％，而普通的说话人适应方法（例如MLLR）无法通过有限的有监督或无监督适应数据有效地工作。另外，本文通过基于模型特征对特征空间进行分割，实现了在大量适应数据中电动汽车性能的发展。

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